Alternative care providers in rheumatoid arthritis patient care: a queueing and simulation analysis
Bibliographic record
Abstract
Patients diagnosed with rheumatoid arthritis require lifelong monitoring by a rheumatologist. Initiation of the disease-modifying anti-rheumatic drug therapy within twelve weeks of the onset of symptoms is crucial to prevent joint damage and functional disability. We examine the impact of the engagement of alternate care providers (ACP) in alleviating delay due to limited rheumatologist capacity. Using queueing theory and discrete-event simulation, we model rheumatologist-only and rheumatologist-with-ACP system configurations as closed, multi-class queueing networks with class switching.Using summary data from an actual rheumatology clinic for illustration, we analyze various parameter conditions to aid clinic managers and policymakers in decisions concerning capacity allocations and feasible patient panel size that impact timeliness of care and resource utilization.Results not only confirm that a substantial increase in RA patient panel size with an ACP involved in the care of follow-up patients but also demonstrates the boundaries for feasible panel sizes and workload allocation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".